The service industry covers a wide variety of activities such as trade, hotels and restaurants, transport, storage and communication, financing, insurance, real estate, business services, community, social and personal services, and services associated with construction. Presently, the quantum at which service requests are generated in various sectors of the service industry is increasing day by day. Handling of these service requests requires assistance from a large number of human professionals/experts. Hence, automated request-response systems that require limited number of human experts are being used in the service industry to cater to the needs of all the service customers.
The existing automated request-response systems make use of standard machine learning and text analysis concepts to handle the service request/user queries. Generally, the user queries can be of two types. The first type of queries comprises of frequently asked questions and known issues which have defined answers available in the automated request-response system. The second type of queries comprises of complicated logic or complex reasoning, for which the answers are not available in the system or which are difficult to comprehend by the system. Hence, such type of complex queries requires human experts to interact with the user for understanding, classifying and the for resolving the complex user queries.
The existing approaches of automated request-response systems, such as chat bot assistants, help in reducing the amount of service requests which are generic or of the first type. However, these systems are not suitable to handle the complex, domain specific service queries or queries of the second type, which can satisfy the service users. Hence, the systems switch to a human-agent mode by connecting user session to a human expert, for example when the automated chat bot or chat system is not able to handle the complicated queries of the user.
In a system where many such human experts are available with varied expertise to handle different type of queries, it is desirable and efficient to choose the human expert who is best suited to the type of query that the user is facing. Hence, there is a need for selection of human experts when dealing with type 2 service queries, thereby the system should be able to connect to the most appropriate human expert seamlessly in real-time.
The challenges mainly faced during dynamic recommendation of experts for resolving queries is to estimate the relative expertise of the available human experts to handle different categories of user queries in an automatic and real-time manner.